Abnormal event detection based on analysis of movement information of video sequence

Wang, Tian; Qiao, Meina; Deng, Yingjun; Zhou, Yi; Wang, Huan; Lyu, Qi; Snoussi, Hichem

doi:10.1016/j.ijleo.2017.07.064

Cited by 49 publications

(8 citation statements)

References 16 publications

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“…Those requirements have promoted the creation of specialized tools, both on equipment and software, to support the surveillance task. The most common approaches include motion detection [8,9], face recognition [5,6,10,11], tracking [12][13][14], loitering detection [15], abandoned luggage detection [16], crowd behavior [17][18][19], and abnormal behavior [20,21]. Prevention and reaction are two primary aims in the surveillance context.…”

Section: Background and Related Workmentioning

confidence: 99%

Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks

et al. 2021

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Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks generate vast amounts of data, and the surveillance staff cannot process all the information in real-time. Human sight has critical limitations. Among those limitations, visual focus is one of the most critical when dealing with surveillance. For example, in a surveillance room, a crime can occur in a different screen segment or on a distinct monitor, and the surveillance staff may overlook it. Our proposal focuses on shoplifting crimes by analyzing situations that an average person will consider as typical conditions, but may eventually lead to a crime. While other approaches identify the crime itself, we instead model suspicious behavior—the one that may occur before the build-up phase of a crime—by detecting precise segments of a video with a high probability of containing a shoplifting crime. By doing so, we provide the staff with more opportunities to act and prevent crime. We implemented a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily action and shoplifting samples. The results are encouraging as the model correctly classifies suspicious behavior in most of the scenarios where it was tested. For example, when classifying suspicious behavior, the best model generated in this work obtains precision and recall values of 0.8571 and 1 in one of the test scenarios, respectively.

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Section: Background and Related Workmentioning

confidence: 99%

Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks

et al. 2021

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“…The limitation of these approaches is that a single classifier is difficult to ensure the accuracy of classification. Wang et al [34] propose to learn the histograms of optical flow orientations of the observed video frames by a hidden Markov model to detect abnormal events in a crowded scene. In order to at least alleviate the impact of label information on supervised or semi-supervised models, the study in [35] proposes an unsupervised algorithm that combines the manifold-based feature with a graph density search mechanism to detect abnormal network events.…”

Section: A Hand-crafted Features-based Modelsmentioning

confidence: 99%

“…We conduct extensive experiments on a widely used abnormal event detection dataset and a coal mining video dataset to evaluate the performance of the proposed DF-ESCC and compare them with several state-of-the-art methods such as SURF+BoW, SIFT+BoW [16], HMM with optical flow [34], CNN-2D+LSTM [43] and CNN-2D+LSTM+SVM [47]. All the experiments are conducted on a machine having a Inter Core (TM) i7-7700HQ processor with 8G memory and a Huawei server having 4-Inter Xeon processors with 8G memory, respectively.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Deng

et al. 2020

IEEE Access

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At present, the existing abnormal event detection models based on deep learning mainly focus on data represented by a vectorial form, which pay little attention to the impact of the internal structure characteristics of feature vector. In addition, a single classifier is difficult to ensure the accuracy of classification. In order to address the above issues, we propose an abnormal event detection hybrid modulation method via feature expectation subgraph calibrating classification in video surveillance scenes in this paper. Our main contribution is to calibrate the classification of a single classifier by constructing feature expectation subgraphs. First, we employ convolutional neural network and long short-term memory models to extract the spatiotemporal features of video frame, and then construct the feature expectation subgraph for each key frame of every video, which could be used to capture the internal sequential and topological relational characteristics of structured feature vector. Second, we project expectation subgraphs on the sparse vector to combine with a support vector classifier to calibrate the results of a linear support vector classifier. Finally, the experiments on a common dataset named UCSDped1 and a coal mining video dataset in comparison with some existing works demonstrate that the performance of the proposed method is better than several the state-of-the-art approaches. INDEX TERMS Abnormal event detection, feature expectation subgraph, calibrating classification, sequential and topological relational characteristics.

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“…Regarding the existing learning models, the approaches consider anomaly detection as a binary classification task, and activities are classified as either normal or abnormal. An extensive amount of literature has been devoted to the field of anomaly detection in videos, and researchers have published valuable survey articles on the literature [1], [3], [19], [20]. The vast majority of anomaly detection methods in videos engage a handcrafted feature extraction stage, followed by establishing a pattern model using videos containing only regular activities.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Videos Using Optical Flow and Convolutional Autoencoder

Duman

Erdem

2019

IEEE Access

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Today, public areas, such as airports, hospitals, city centers are monitored by surveillance systems. The widespread use of surveillance systems reduces security concerns while creating an amount of video data that cannot be examined by people in real-time. Therefore, the concept of automatic understanding of video activities has raised the standards of security camera systems. In this paper, we propose a framework (OF-ConvAE-LSTM) to detect anomalies using Convolutional Autoencoder and Convolutional Long Short-Term Memory in an unsupervised manner. Besides the deep learning model, the feature extraction stage based on dense optical flow is applied in the framework to obtain the velocity and direction information of foreground objects. The experiments were carried out on three popular public datasets consisting of Avenue, UCSD Ped1, and UCSD Peds2. The experimental results have shown that the proposed framework models the complex distribution of the pattern of regular motion changes with high accuracy. Besides, this method was observed to outperform state-of-the-art approaches based on unsupervised and semi-supervised deep learning models.INDEX TERMS Abnormal event detection, convolutional autoencoder, long short-term memory, optical flow.

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Abnormal event detection based on analysis of movement information of video sequence

Cited by 49 publications

References 16 publications

Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks

Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Anomaly Detection in Videos Using Optical Flow and Convolutional Autoencoder

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